Overview

Dataset statistics

Number of variables16
Number of observations15120
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.8 MiB
Average record size in memory128.0 B

Variable types

Numeric14
Categorical2

Alerts

Aspect is highly overall correlated with Hillshade_3pmHigh correlation
Climatic_Zone is highly overall correlated with Elevation and 2 other fieldsHigh correlation
Cover_Type is highly overall correlated with Wilderness_AreaHigh correlation
Distance_to_Hydrology is highly overall correlated with Horizontal_Distance_To_Hydrology and 1 other fieldsHigh correlation
Elevation is highly overall correlated with Climatic_Zone and 4 other fieldsHigh correlation
Geologic_Zone is highly overall correlated with Climatic_ZoneHigh correlation
Hillshade_3pm is highly overall correlated with Aspect and 2 other fieldsHigh correlation
Hillshade_9am is highly overall correlated with Hillshade_3pmHigh correlation
Hillshade_Noon is highly overall correlated with Hillshade_3pm and 1 other fieldsHigh correlation
Horizontal_Distance_To_Fire_Points is highly overall correlated with ElevationHigh correlation
Horizontal_Distance_To_Hydrology is highly overall correlated with Distance_to_Hydrology and 1 other fieldsHigh correlation
Horizontal_Distance_To_Roadways is highly overall correlated with ElevationHigh correlation
Slope is highly overall correlated with Hillshade_NoonHigh correlation
Soil_Type is highly overall correlated with Climatic_Zone and 2 other fieldsHigh correlation
Vertical_Distance_To_Hydrology is highly overall correlated with Distance_to_Hydrology and 1 other fieldsHigh correlation
Wilderness_Area is highly overall correlated with Cover_Type and 2 other fieldsHigh correlation
Geologic_Zone is highly imbalanced (62.8%)Imbalance
Horizontal_Distance_To_Hydrology has 1506 (10.0%) zerosZeros
Vertical_Distance_To_Hydrology has 1801 (11.9%) zerosZeros
Distance_to_Hydrology has 1506 (10.0%) zerosZeros

Reproduction

Analysis started2024-02-18 10:40:55.322111
Analysis finished2024-02-18 10:41:24.288046
Duration28.97 seconds
Software versionydata-profiling v0.0.dev0
Download configurationconfig.json

Variables

Elevation
Real number (ℝ)

HIGH CORRELATION 

Distinct1676
Distinct (%)11.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2748.6499
Minimum1877
Maximum3850
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size118.2 KiB
2024-02-18T11:41:24.495269image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1877
5-th percentile2115
Q12373
median2754
Q33109
95-th percentile3397
Maximum3850
Range1973
Interquartile range (IQR)736

Descriptive statistics

Standard deviation419.00959
Coefficient of variation (CV)0.15244196
Kurtosis-1.0884902
Mean2748.6499
Median Absolute Deviation (MAD)370
Skewness0.074424175
Sum41559587
Variance175569.04
MonotonicityNot monotonic
2024-02-18T11:41:24.652085image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2821 27
 
0.2%
2797 23
 
0.2%
2328 22
 
0.1%
2850 22
 
0.1%
2332 22
 
0.1%
3372 22
 
0.1%
2702 21
 
0.1%
2972 21
 
0.1%
3393 21
 
0.1%
2397 21
 
0.1%
Other values (1666) 14898
98.5%
ValueCountFrequency (%)
1877 1
 
< 0.1%
1889 1
 
< 0.1%
1890 1
 
< 0.1%
1896 3
< 0.1%
1899 1
 
< 0.1%
1906 2
< 0.1%
1911 3
< 0.1%
1913 1
 
< 0.1%
1914 2
< 0.1%
1915 1
 
< 0.1%
ValueCountFrequency (%)
3850 1
< 0.1%
3849 1
< 0.1%
3848 1
< 0.1%
3845 2
< 0.1%
3840 1
< 0.1%
3836 1
< 0.1%
3835 1
< 0.1%
3829 2
< 0.1%
3821 1
< 0.1%
3819 2
< 0.1%

Aspect
Real number (ℝ)

HIGH CORRELATION 

Distinct361
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean155.83452
Minimum0
Maximum360
Zeros101
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size118.2 KiB
2024-02-18T11:41:24.812275image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile13
Q165
median125
Q3257
95-th percentile344
Maximum360
Range360
Interquartile range (IQR)192

Descriptive statistics

Standard deviation109.74537
Coefficient of variation (CV)0.704243
Kurtosis-1.1221267
Mean155.83452
Median Absolute Deviation (MAD)76
Skewness0.46644858
Sum2356218
Variance12044.047
MonotonicityNot monotonic
2024-02-18T11:41:24.970981image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
45 128
 
0.8%
90 114
 
0.8%
0 101
 
0.7%
135 89
 
0.6%
72 84
 
0.6%
108 83
 
0.5%
63 82
 
0.5%
18 82
 
0.5%
104 79
 
0.5%
101 78
 
0.5%
Other values (351) 14200
93.9%
ValueCountFrequency (%)
0 101
0.7%
1 34
 
0.2%
2 41
0.3%
3 51
0.3%
4 47
0.3%
5 55
0.4%
6 67
0.4%
7 43
0.3%
8 53
0.4%
9 58
0.4%
ValueCountFrequency (%)
360 2
 
< 0.1%
359 39
0.3%
358 43
0.3%
357 47
0.3%
356 53
0.4%
355 63
0.4%
354 57
0.4%
353 47
0.3%
352 55
0.4%
351 57
0.4%

Slope
Real number (ℝ)

HIGH CORRELATION 

Distinct51
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.556746
Minimum0
Maximum50
Zeros11
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size118.2 KiB
2024-02-18T11:41:25.141991image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4.95
Q110
median15
Q322
95-th percentile32
Maximum50
Range50
Interquartile range (IQR)12

Descriptive statistics

Standard deviation8.5346018
Coefficient of variation (CV)0.51547579
Kurtosis-0.25286265
Mean16.556746
Median Absolute Deviation (MAD)6
Skewness0.53256718
Sum250338
Variance72.839428
MonotonicityNot monotonic
2024-02-18T11:41:25.321795image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11 748
 
4.9%
12 718
 
4.7%
13 708
 
4.7%
10 703
 
4.6%
9 688
 
4.6%
15 672
 
4.4%
14 636
 
4.2%
16 626
 
4.1%
8 614
 
4.1%
17 586
 
3.9%
Other values (41) 8421
55.7%
ValueCountFrequency (%)
0 11
 
0.1%
1 55
 
0.4%
2 134
 
0.9%
3 235
 
1.6%
4 321
2.1%
5 395
2.6%
6 460
3.0%
7 560
3.7%
8 614
4.1%
9 688
4.6%
ValueCountFrequency (%)
50 1
 
< 0.1%
49 5
 
< 0.1%
48 2
 
< 0.1%
47 2
 
< 0.1%
46 7
< 0.1%
45 7
< 0.1%
44 5
 
< 0.1%
43 11
0.1%
42 8
0.1%
41 16
0.1%

Horizontal_Distance_To_Hydrology
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct397
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean228.37652
Minimum0
Maximum1376
Zeros1506
Zeros (%)10.0%
Negative0
Negative (%)0.0%
Memory size118.2 KiB
2024-02-18T11:41:25.567376image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q167
median180
Q3330
95-th percentile636
Maximum1376
Range1376
Interquartile range (IQR)263

Descriptive statistics

Standard deviation209.19638
Coefficient of variation (CV)0.91601527
Kurtosis2.5208781
Mean228.37652
Median Absolute Deviation (MAD)120
Skewness1.4388585
Sum3453053
Variance43763.126
MonotonicityNot monotonic
2024-02-18T11:41:25.721893image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1506
 
10.0%
30 1220
 
8.1%
150 539
 
3.6%
60 508
 
3.4%
42 440
 
2.9%
67 419
 
2.8%
108 389
 
2.6%
85 367
 
2.4%
90 330
 
2.2%
120 289
 
1.9%
Other values (387) 9113
60.3%
ValueCountFrequency (%)
0 1506
10.0%
30 1220
8.1%
42 440
 
2.9%
60 508
 
3.4%
67 419
 
2.8%
85 367
 
2.4%
90 330
 
2.2%
95 273
 
1.8%
108 389
 
2.6%
120 289
 
1.9%
ValueCountFrequency (%)
1376 1
< 0.1%
1355 1
< 0.1%
1269 1
< 0.1%
1259 1
< 0.1%
1234 1
< 0.1%
1201 1
< 0.1%
1190 1
< 0.1%
1189 1
< 0.1%
1187 2
< 0.1%
1181 1
< 0.1%

Vertical_Distance_To_Hydrology
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct433
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean51.311706
Minimum-135
Maximum570
Zeros1801
Zeros (%)11.9%
Negative1147
Negative (%)7.6%
Memory size118.2 KiB
2024-02-18T11:41:25.895673image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum-135
5-th percentile-5
Q15
median32
Q380
95-th percentile176
Maximum570
Range705
Interquartile range (IQR)75

Descriptive statistics

Standard deviation61.520488
Coefficient of variation (CV)1.1989562
Kurtosis3.2213024
Mean51.311706
Median Absolute Deviation (MAD)32
Skewness1.5099205
Sum775833
Variance3784.7704
MonotonicityNot monotonic
2024-02-18T11:41:26.071958image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1801
 
11.9%
5 226
 
1.5%
4 207
 
1.4%
6 204
 
1.3%
7 197
 
1.3%
2 179
 
1.2%
9 174
 
1.2%
13 172
 
1.1%
8 168
 
1.1%
11 168
 
1.1%
Other values (423) 11624
76.9%
ValueCountFrequency (%)
-135 1
< 0.1%
-130 1
< 0.1%
-118 1
< 0.1%
-114 1
< 0.1%
-113 1
< 0.1%
-111 1
< 0.1%
-110 1
< 0.1%
-108 1
< 0.1%
-106 1
< 0.1%
-105 1
< 0.1%
ValueCountFrequency (%)
570 1
 
< 0.1%
551 1
 
< 0.1%
410 1
 
< 0.1%
402 1
 
< 0.1%
401 3
< 0.1%
396 1
 
< 0.1%
395 1
 
< 0.1%
391 2
< 0.1%
387 1
 
< 0.1%
386 1
 
< 0.1%

Horizontal_Distance_To_Roadways
Real number (ℝ)

HIGH CORRELATION 

Distinct3274
Distinct (%)21.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1717.9777
Minimum0
Maximum6803
Zeros8
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size118.2 KiB
2024-02-18T11:41:26.281887image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile242
Q1760
median1315
Q32292
95-th percentile4624
Maximum6803
Range6803
Interquartile range (IQR)1532

Descriptive statistics

Standard deviation1330.2635
Coefficient of variation (CV)0.77431939
Kurtosis1.0309417
Mean1717.9777
Median Absolute Deviation (MAD)689
Skewness1.2477486
Sum25975823
Variance1769600.8
MonotonicityNot monotonic
2024-02-18T11:41:26.484764image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
150 75
 
0.5%
240 53
 
0.4%
618 47
 
0.3%
900 46
 
0.3%
390 43
 
0.3%
750 42
 
0.3%
277 41
 
0.3%
300 41
 
0.3%
450 39
 
0.3%
120 39
 
0.3%
Other values (3264) 14654
96.9%
ValueCountFrequency (%)
0 8
 
0.1%
30 17
0.1%
42 4
 
< 0.1%
60 19
0.1%
67 9
 
0.1%
85 14
 
0.1%
90 25
0.2%
95 22
0.1%
108 31
0.2%
120 39
0.3%
ValueCountFrequency (%)
6803 1
< 0.1%
6764 1
< 0.1%
6717 1
< 0.1%
6632 1
< 0.1%
6595 1
< 0.1%
6574 1
< 0.1%
6557 1
< 0.1%
6518 1
< 0.1%
6503 1
< 0.1%
6454 1
< 0.1%

Hillshade_9am
Real number (ℝ)

HIGH CORRELATION 

Distinct176
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean213.02884
Minimum52
Maximum254
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size118.2 KiB
2024-02-18T11:41:26.722829image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum52
5-th percentile151.95
Q1197
median220
Q3236
95-th percentile251
Maximum254
Range202
Interquartile range (IQR)39

Descriptive statistics

Standard deviation30.638406
Coefficient of variation (CV)0.14382281
Kurtosis1.0729703
Mean213.02884
Median Absolute Deviation (MAD)19
Skewness-1.0754914
Sum3220996
Variance938.71191
MonotonicityNot monotonic
2024-02-18T11:41:27.227307image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
228 285
 
1.9%
226 280
 
1.9%
230 264
 
1.7%
224 257
 
1.7%
227 252
 
1.7%
236 242
 
1.6%
234 234
 
1.5%
229 233
 
1.5%
233 231
 
1.5%
232 231
 
1.5%
Other values (166) 12611
83.4%
ValueCountFrequency (%)
52 1
< 0.1%
56 1
< 0.1%
68 1
< 0.1%
69 1
< 0.1%
71 1
< 0.1%
72 2
< 0.1%
75 1
< 0.1%
76 1
< 0.1%
77 1
< 0.1%
81 2
< 0.1%
ValueCountFrequency (%)
254 185
1.2%
253 208
1.4%
252 203
1.3%
251 209
1.4%
250 213
1.4%
249 224
1.5%
248 204
1.3%
247 200
1.3%
246 181
1.2%
245 188
1.2%

Hillshade_Noon
Real number (ℝ)

HIGH CORRELATION 

Distinct140
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean218.86574
Minimum99
Maximum254
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size118.2 KiB
2024-02-18T11:41:27.414790image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum99
5-th percentile174
Q1207
median223
Q3235
95-th percentile250
Maximum254
Range155
Interquartile range (IQR)28

Descriptive statistics

Standard deviation22.797288
Coefficient of variation (CV)0.10416106
Kurtosis1.0263935
Mean218.86574
Median Absolute Deviation (MAD)14
Skewness-0.94274704
Sum3309250
Variance519.71635
MonotonicityNot monotonic
2024-02-18T11:41:27.612954image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
228 331
 
2.2%
225 331
 
2.2%
226 322
 
2.1%
224 309
 
2.0%
232 303
 
2.0%
231 301
 
2.0%
218 298
 
2.0%
229 295
 
2.0%
223 294
 
1.9%
221 289
 
1.9%
Other values (130) 12047
79.7%
ValueCountFrequency (%)
99 2
< 0.1%
102 1
 
< 0.1%
107 2
< 0.1%
111 2
< 0.1%
112 1
 
< 0.1%
115 1
 
< 0.1%
118 1
 
< 0.1%
119 1
 
< 0.1%
120 1
 
< 0.1%
121 3
< 0.1%
ValueCountFrequency (%)
254 134
0.9%
253 143
0.9%
252 157
1.0%
251 162
1.1%
250 163
1.1%
249 178
1.2%
248 189
1.2%
247 195
1.3%
246 212
1.4%
245 210
1.4%

Hillshade_3pm
Real number (ℝ)

HIGH CORRELATION 

Distinct248
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean134.47712
Minimum0
Maximum251
Zeros95
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size118.2 KiB
2024-02-18T11:41:27.812852image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile51
Q1106
median138
Q3166
95-th percentile207
Maximum251
Range251
Interquartile range (IQR)60

Descriptive statistics

Standard deviation46.070054
Coefficient of variation (CV)0.34258657
Kurtosis-0.060996158
Mean134.47712
Median Absolute Deviation (MAD)30
Skewness-0.35341849
Sum2033294
Variance2122.4499
MonotonicityNot monotonic
2024-02-18T11:41:27.986060image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
145 178
 
1.2%
143 166
 
1.1%
146 165
 
1.1%
149 162
 
1.1%
142 161
 
1.1%
139 159
 
1.1%
135 151
 
1.0%
132 151
 
1.0%
148 148
 
1.0%
128 148
 
1.0%
Other values (238) 13531
89.5%
ValueCountFrequency (%)
0 95
0.6%
2 1
 
< 0.1%
3 4
 
< 0.1%
4 1
 
< 0.1%
5 3
 
< 0.1%
6 3
 
< 0.1%
8 2
 
< 0.1%
9 4
 
< 0.1%
10 5
 
< 0.1%
11 3
 
< 0.1%
ValueCountFrequency (%)
251 1
 
< 0.1%
249 1
 
< 0.1%
248 1
 
< 0.1%
246 1
 
< 0.1%
245 2
 
< 0.1%
244 6
< 0.1%
243 3
< 0.1%
242 3
< 0.1%
241 4
< 0.1%
240 5
< 0.1%

Horizontal_Distance_To_Fire_Points
Real number (ℝ)

HIGH CORRELATION 

Distinct2764
Distinct (%)18.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1527.3578
Minimum0
Maximum7095
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size118.2 KiB
2024-02-18T11:41:28.172232image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile295
Q1750
median1266
Q32002
95-th percentile3747
Maximum7095
Range7095
Interquartile range (IQR)1252

Descriptive statistics

Standard deviation1116.637
Coefficient of variation (CV)0.73109064
Kurtosis3.5414672
Mean1527.3578
Median Absolute Deviation (MAD)592
Skewness1.6516837
Sum23093650
Variance1246878.2
MonotonicityNot monotonic
2024-02-18T11:41:28.361752image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
541 49
 
0.3%
618 47
 
0.3%
819 45
 
0.3%
607 45
 
0.3%
242 45
 
0.3%
335 44
 
0.3%
997 40
 
0.3%
342 39
 
0.3%
872 39
 
0.3%
900 38
 
0.3%
Other values (2754) 14689
97.1%
ValueCountFrequency (%)
0 1
 
< 0.1%
30 18
0.1%
42 7
 
< 0.1%
60 7
 
< 0.1%
67 20
0.1%
85 6
 
< 0.1%
90 14
0.1%
95 21
0.1%
108 24
0.2%
120 10
0.1%
ValueCountFrequency (%)
7095 1
< 0.1%
7061 1
< 0.1%
6961 1
< 0.1%
6960 1
< 0.1%
6906 1
< 0.1%
6895 1
< 0.1%
6881 1
< 0.1%
6811 1
< 0.1%
6810 1
< 0.1%
6809 1
< 0.1%

Cover_Type
Real number (ℝ)

HIGH CORRELATION 

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size118.2 KiB
2024-02-18T11:41:28.501080image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q36
95-th percentile7
Maximum7
Range6
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.0000661
Coefficient of variation (CV)0.50001654
Kurtosis-1.2500165
Mean4
Median Absolute Deviation (MAD)2
Skewness0
Sum60480
Variance4.0002646
MonotonicityIncreasing
2024-02-18T11:41:28.637222image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
1 2160
14.3%
2 2160
14.3%
3 2160
14.3%
4 2160
14.3%
5 2160
14.3%
6 2160
14.3%
7 2160
14.3%
ValueCountFrequency (%)
1 2160
14.3%
2 2160
14.3%
3 2160
14.3%
4 2160
14.3%
5 2160
14.3%
6 2160
14.3%
7 2160
14.3%
ValueCountFrequency (%)
7 2160
14.3%
6 2160
14.3%
5 2160
14.3%
4 2160
14.3%
3 2160
14.3%
2 2160
14.3%
1 2160
14.3%

Soil_Type
Real number (ℝ)

HIGH CORRELATION 

Distinct39
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19.067394
Minimum1
Maximum40
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size118.2 KiB
2024-02-18T11:41:28.802287image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q110
median17
Q330
95-th percentile39
Maximum40
Range39
Interquartile range (IQR)20

Descriptive statistics

Standard deviation12.62976
Coefficient of variation (CV)0.66237472
Kurtosis-1.4119564
Mean19.067394
Median Absolute Deviation (MAD)12
Skewness0.15684829
Sum288299
Variance159.51084
MonotonicityNot monotonic
2024-02-18T11:41:29.007813image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
10 2096
 
13.9%
29 1308
 
8.7%
3 1006
 
6.7%
4 839
 
5.5%
38 744
 
4.9%
23 742
 
4.9%
30 736
 
4.9%
6 679
 
4.5%
32 663
 
4.4%
17 640
 
4.2%
Other values (29) 5667
37.5%
ValueCountFrequency (%)
1 339
 
2.2%
2 627
 
4.1%
3 1006
6.7%
4 839
5.5%
5 181
 
1.2%
6 679
 
4.5%
7 1
 
< 0.1%
8 2
 
< 0.1%
9 4
 
< 0.1%
10 2096
13.9%
ValueCountFrequency (%)
40 456
3.0%
39 634
4.2%
38 744
4.9%
37 32
 
0.2%
36 14
 
0.1%
35 103
 
0.7%
34 18
 
0.1%
33 619
4.1%
32 663
4.4%
31 304
2.0%

Wilderness_Area
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size886.1 KiB
3.0
6302 
4.0
4681 
1.0
3568 
2.0
 
569

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters45360
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row3.0
3rd row1.0
4th row2.0
5th row1.0

Common Values

ValueCountFrequency (%)
3.0 6302
41.7%
4.0 4681
31.0%
1.0 3568
23.6%
2.0 569
 
3.8%

Length

2024-02-18T11:41:29.183316image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-18T11:41:29.345792image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
3.0 6302
41.7%
4.0 4681
31.0%
1.0 3568
23.6%
2.0 569
 
3.8%

Most occurring characters

ValueCountFrequency (%)
. 15120
33.3%
0 15120
33.3%
3 6302
13.9%
4 4681
 
10.3%
1 3568
 
7.9%
2 569
 
1.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 30240
66.7%
Other Punctuation 15120
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 15120
50.0%
3 6302
20.8%
4 4681
 
15.5%
1 3568
 
11.8%
2 569
 
1.9%
Other Punctuation
ValueCountFrequency (%)
. 15120
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 45360
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 15120
33.3%
0 15120
33.3%
3 6302
13.9%
4 4681
 
10.3%
1 3568
 
7.9%
2 569
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 45360
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 15120
33.3%
0 15120
33.3%
3 6302
13.9%
4 4681
 
10.3%
1 3568
 
7.9%
2 569
 
1.3%

Climatic_Zone
Real number (ℝ)

HIGH CORRELATION 

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.196627
Minimum2
Maximum8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size118.2 KiB
2024-02-18T11:41:29.562098image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile2
Q14
median6
Q37
95-th percentile8
Maximum8
Range6
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.2325368
Coefficient of variation (CV)0.42961266
Kurtosis-1.4866956
Mean5.196627
Median Absolute Deviation (MAD)2
Skewness-0.3009104
Sum78573
Variance4.9842204
MonotonicityNot monotonic
2024-02-18T11:41:29.692345image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
7 5251
34.7%
2 3671
24.3%
4 3249
21.5%
8 1983
 
13.1%
6 790
 
5.2%
5 173
 
1.1%
3 3
 
< 0.1%
ValueCountFrequency (%)
2 3671
24.3%
3 3
 
< 0.1%
4 3249
21.5%
5 173
 
1.1%
6 790
 
5.2%
7 5251
34.7%
8 1983
 
13.1%
ValueCountFrequency (%)
8 1983
 
13.1%
7 5251
34.7%
6 790
 
5.2%
5 173
 
1.1%
4 3249
21.5%
3 3
 
< 0.1%
2 3671
24.3%

Geologic_Zone
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size886.1 KiB
7.0
12925 
1.0
 
1114
2.0
 
1078
5.0
 
3

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters45360
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row7.0
2nd row7.0
3rd row7.0
4th row2.0
5th row7.0

Common Values

ValueCountFrequency (%)
7.0 12925
85.5%
1.0 1114
 
7.4%
2.0 1078
 
7.1%
5.0 3
 
< 0.1%

Length

2024-02-18T11:41:29.832189image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-18T11:41:30.002855image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
7.0 12925
85.5%
1.0 1114
 
7.4%
2.0 1078
 
7.1%
5.0 3
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
. 15120
33.3%
0 15120
33.3%
7 12925
28.5%
1 1114
 
2.5%
2 1078
 
2.4%
5 3
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 30240
66.7%
Other Punctuation 15120
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 15120
50.0%
7 12925
42.7%
1 1114
 
3.7%
2 1078
 
3.6%
5 3
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
. 15120
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 45360
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 15120
33.3%
0 15120
33.3%
7 12925
28.5%
1 1114
 
2.5%
2 1078
 
2.4%
5 3
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 45360
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 15120
33.3%
0 15120
33.3%
7 12925
28.5%
1 1114
 
2.5%
2 1078
 
2.4%
5 3
 
< 0.1%

Distance_to_Hydrology
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct7027
Distinct (%)46.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean237.1788
Minimum0
Maximum1390.8936
Zeros1506
Zeros (%)10.0%
Negative0
Negative (%)0.0%
Memory size118.2 KiB
2024-02-18T11:41:30.157003image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q167.742158
median188.80943
Q3344.95797
95-th percentile648.95316
Maximum1390.8936
Range1390.8936
Interquartile range (IQR)277.21581

Descriptive statistics

Standard deviation214.66891
Coefficient of variation (CV)0.90509318
Kurtosis2.3789779
Mean237.1788
Median Absolute Deviation (MAD)128.75529
Skewness1.3933065
Sum3586143.5
Variance46082.743
MonotonicityNot monotonic
2024-02-18T11:41:30.351775image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1506
 
10.0%
30 131
 
0.9%
30.01666204 121
 
0.8%
30.41381265 118
 
0.8%
30.14962686 110
 
0.7%
30.59411708 103
 
0.7%
30.06659276 98
 
0.6%
30.2654919 92
 
0.6%
30.8058436 81
 
0.5%
31.04834939 59
 
0.4%
Other values (7017) 12701
84.0%
ValueCountFrequency (%)
0 1506
10.0%
30 131
 
0.9%
30.01666204 121
 
0.8%
30.06659276 98
 
0.6%
30.14962686 110
 
0.7%
30.2654919 92
 
0.6%
30.41381265 118
 
0.8%
30.59411708 103
 
0.7%
30.8058436 81
 
0.5%
31.04834939 59
 
0.4%
ValueCountFrequency (%)
1390.893598 1
< 0.1%
1368.395411 1
< 0.1%
1301.49222 1
< 0.1%
1275.067449 1
< 0.1%
1271.802265 1
< 0.1%
1244.458517 1
< 0.1%
1239.560003 1
< 0.1%
1223.240369 1
< 0.1%
1222.388236 1
< 0.1%
1219.99877 1
< 0.1%

Interactions

2024-02-18T11:41:21.722053image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-18T11:40:57.026189image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-18T11:40:59.032363image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-18T11:41:01.012520image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-18T11:41:02.653476image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-18T11:41:04.531765image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-18T11:41:06.347275image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-18T11:41:08.226673image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-18T11:41:10.221713image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-18T11:41:12.271994image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-18T11:41:14.035834image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-18T11:41:16.008928image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-18T11:41:17.983819image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-18T11:41:19.852013image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-18T11:41:21.873520image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-18T11:40:57.177890image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-18T11:40:59.155615image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-18T11:41:01.122275image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-18T11:41:02.771806image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-18T11:41:04.656876image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-18T11:41:06.484367image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-18T11:41:08.375340image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-18T11:41:10.367618image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-18T11:41:12.496934image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-18T11:41:14.157224image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-18T11:41:16.144762image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-18T11:41:18.102192image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-18T11:41:19.986701image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-18T11:41:22.053600image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-18T11:40:57.312268image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-18T11:40:59.304838image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-18T11:41:01.251728image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-18T11:41:02.901185image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-18T11:41:04.784938image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-18T11:41:06.692120image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-18T11:41:08.526256image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-18T11:41:10.492199image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-18T11:41:12.622102image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-18T11:41:14.277033image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-18T11:41:16.276783image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-18T11:41:18.235663image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-18T11:41:20.119009image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-18T11:41:22.215088image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-18T11:40:57.420606image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-18T11:40:59.442094image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-18T11:41:01.355338image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-18T11:41:03.011927image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-18T11:41:04.892064image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-18T11:41:06.823147image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-18T11:41:08.661840image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-18T11:41:10.603849image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-18T11:41:12.742322image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-18T11:41:14.386290image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-18T11:41:16.422033image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-18T11:41:18.357124image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-18T11:41:20.235843image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-18T11:41:22.341996image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-18T11:40:57.553988image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-18T11:40:59.619595image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-18T11:41:01.492231image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-18T11:41:03.141982image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-18T11:41:05.016987image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-18T11:41:06.957559image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-18T11:41:08.802104image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-18T11:41:10.746434image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-18T11:41:12.872297image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-18T11:41:14.522006image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-18T11:41:16.572150image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-18T11:41:18.502320image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-18T11:41:20.372034image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-18T11:41:22.483640image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-18T11:40:57.686763image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-18T11:40:59.751963image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-18T11:41:01.624929image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-18T11:41:03.272209image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-18T11:41:05.136841image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-18T11:41:07.082079image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-18T11:41:08.924429image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-18T11:41:10.867573image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-18T11:41:12.996604image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-18T11:41:14.643323image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-18T11:41:16.692277image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-18T11:41:18.645295image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-18T11:41:20.506800image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-18T11:41:22.619159image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-18T11:40:57.808744image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-18T11:40:59.905057image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-18T11:41:01.763813image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-18T11:41:03.392040image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-18T11:41:05.259150image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-18T11:41:07.202224image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-18T11:41:09.072249image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-18T11:41:10.992070image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-18T11:41:13.121847image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-18T11:41:14.888212image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-18T11:41:16.817165image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-18T11:41:18.777542image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-18T11:41:20.631977image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-18T11:41:22.783603image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-18T11:40:57.953140image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-18T11:41:00.041869image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-18T11:41:01.877188image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-18T11:41:03.651846image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-18T11:41:05.392006image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-18T11:41:07.334376image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-18T11:41:09.202038image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-18T11:41:11.161830image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-18T11:41:13.243982image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-18T11:41:15.023250image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-18T11:41:16.947231image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-18T11:41:18.922313image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-18T11:41:20.752417image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-18T11:41:22.921987image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-18T11:40:58.106776image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-18T11:41:00.202215image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-18T11:41:02.003171image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-18T11:41:03.786175image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-18T11:41:05.514641image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-18T11:41:07.453414image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-18T11:41:09.334174image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-18T11:41:11.336351image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-18T11:41:13.353364image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-18T11:41:15.167198image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-18T11:41:17.072038image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-18T11:41:19.050476image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-18T11:41:20.892076image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-18T11:41:23.042168image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-18T11:40:58.241913image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-18T11:41:00.322560image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-18T11:41:02.103342image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-18T11:41:03.892183image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-18T11:41:05.626889image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-18T11:41:07.575659image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-18T11:41:09.442061image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-18T11:41:11.536241image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-18T11:41:13.472159image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-18T11:41:15.293455image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-18T11:41:17.196090image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-18T11:41:19.162105image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-18T11:41:21.012221image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-18T11:41:23.179232image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-18T11:40:58.384988image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-18T11:41:00.461895image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-18T11:41:02.220432image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-18T11:41:04.031820image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-18T11:41:05.801824image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-18T11:41:07.697278image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-18T11:41:09.741542image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-18T11:41:11.695241image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-18T11:41:13.592329image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-18T11:41:15.417271image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-18T11:41:17.301784image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-18T11:41:19.312150image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-18T11:41:21.144268image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-18T11:41:23.307157image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-18T11:40:58.641997image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-18T11:41:00.584683image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-18T11:41:02.322204image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-18T11:41:04.153483image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-18T11:41:05.942073image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-18T11:41:07.826900image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-18T11:41:09.853610image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-18T11:41:11.847249image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-18T11:41:13.693213image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-18T11:41:15.622128image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-18T11:41:17.635456image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-18T11:41:19.445657image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-18T11:41:21.274853image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-18T11:41:23.462179image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-18T11:40:58.789931image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-18T11:41:00.727232image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-18T11:41:02.434578image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-18T11:41:04.286452image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-18T11:41:06.084270image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-18T11:41:07.961884image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-18T11:41:09.982218image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-18T11:41:11.991876image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-18T11:41:13.812128image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-18T11:41:15.754149image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-18T11:41:17.753584image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-18T11:41:19.586144image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-18T11:41:21.411938image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-18T11:41:23.597379image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-18T11:40:58.904084image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-18T11:41:00.855585image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-18T11:41:02.542072image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-18T11:41:04.401772image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-18T11:41:06.206886image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-18T11:41:08.087354image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-18T11:41:10.107017image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-18T11:41:12.132077image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-18T11:41:13.921870image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-18T11:41:15.872518image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-18T11:41:17.872198image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-18T11:41:19.711726image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-18T11:41:21.576908image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Correlations

2024-02-18T11:41:30.548134image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
AspectClimatic_ZoneCover_TypeDistance_to_HydrologyElevationGeologic_ZoneHillshade_3pmHillshade_9amHillshade_NoonHorizontal_Distance_To_Fire_PointsHorizontal_Distance_To_HydrologyHorizontal_Distance_To_RoadwaysSlopeSoil_TypeVertical_Distance_To_HydrologyWilderness_Area
Aspect1.000-0.0310.0060.026-0.0160.0630.618-0.4110.399-0.0780.0240.0550.054-0.0310.0600.167
Climatic_Zone-0.0311.0000.0760.1670.8150.8000.083-0.0570.0490.3930.1820.476-0.2730.967-0.0390.439
Cover_Type0.0060.0761.000-0.0420.0040.260-0.040-0.004-0.090-0.060-0.048-0.0790.0930.0840.0540.522
Distance_to_Hydrology0.0260.167-0.0421.0000.3760.1750.036-0.0330.0170.1670.9980.1300.0370.2120.7210.156
Elevation-0.0160.8150.0040.3761.0000.2500.0820.0330.2060.5140.3940.601-0.3160.8110.0990.595
Geologic_Zone0.0630.8000.2600.1750.2501.000-0.1490.078-0.169-0.0360.308-0.0690.275-0.0400.3550.164
Hillshade_3pm0.6180.083-0.0400.0360.082-0.1491.000-0.8370.5840.0200.0440.179-0.2940.068-0.0380.157
Hillshade_9am-0.411-0.057-0.004-0.0330.0330.078-0.8371.000-0.1390.049-0.034-0.0580.000-0.038-0.0480.174
Hillshade_Noon0.3990.049-0.0900.0170.206-0.1690.584-0.1391.0000.1080.0340.256-0.5390.050-0.1520.155
Horizontal_Distance_To_Fire_Points-0.0780.393-0.0600.1670.514-0.0360.0200.0490.1081.0000.1810.432-0.2430.389-0.0040.340
Horizontal_Distance_To_Hydrology0.0240.182-0.0480.9980.3940.3080.044-0.0340.0340.1811.0000.1460.0080.2260.6940.162
Horizontal_Distance_To_Roadways0.0550.476-0.0790.1300.601-0.0690.179-0.0580.2560.4320.1461.000-0.2900.456-0.0390.394
Slope0.054-0.2730.0930.037-0.3160.275-0.2940.000-0.539-0.2430.008-0.2901.000-0.2450.3250.186
Soil_Type-0.0310.9670.0840.2120.811-0.0400.068-0.0380.0500.3890.2260.456-0.2451.0000.0170.503
Vertical_Distance_To_Hydrology0.060-0.0390.0540.7210.0990.355-0.038-0.048-0.152-0.0040.694-0.0390.3250.0171.0000.095
Wilderness_Area0.1670.4390.5220.1560.5950.1640.1570.1740.1550.3400.1620.3940.1860.5030.0951.000

Missing values

2024-02-18T11:41:23.802126image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-02-18T11:41:24.121730image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

ElevationAspectSlopeHorizontal_Distance_To_HydrologyVertical_Distance_To_HydrologyHorizontal_Distance_To_RoadwaysHillshade_9amHillshade_NoonHillshade_3pmHorizontal_Distance_To_Fire_PointsCover_TypeSoil_TypeWilderness_AreaClimatic_ZoneGeologic_ZoneDistance_to_Hydrology
02881.0130.022.0210.054.01020.0250.0221.088.0342.01.030.01.07.07.0216.831732
13005.0351.014.0242.0-16.01371.0194.0215.0159.0842.01.024.03.07.07.0242.528349
23226.063.014.0618.02.01092.0232.0210.0107.02018.01.029.01.07.07.0618.003236
33298.0317.08.0661.060.0752.0198.0233.0174.01248.01.023.02.07.02.0663.717560
43080.035.06.0175.026.03705.0219.0227.0144.02673.01.024.01.07.07.0176.920886
52772.029.011.090.04.0808.0216.0216.0134.03034.01.024.03.07.07.090.088845
62998.0346.09.0108.03.02648.0203.0226.0161.02097.01.022.03.07.02.0108.041659
73214.056.010.0488.059.04522.0227.0218.0124.02563.01.029.01.07.07.0491.553659
83123.054.013.0210.017.03278.0227.0211.0114.05181.01.020.01.07.01.0210.686972
92972.012.015.0365.069.04392.0204.0209.0140.04991.01.029.01.07.07.0371.464669
ElevationAspectSlopeHorizontal_Distance_To_HydrologyVertical_Distance_To_HydrologyHorizontal_Distance_To_RoadwaysHillshade_9amHillshade_NoonHillshade_3pmHorizontal_Distance_To_Fire_PointsCover_TypeSoil_TypeWilderness_AreaClimatic_ZoneGeologic_ZoneDistance_to_Hydrology
151103344.0157.07.0134.022.01771.0228.0241.0146.03188.07.038.01.08.07.0135.793962
151113225.0104.04.060.0-2.02032.0226.0234.0143.0108.07.035.03.08.07.060.033324
151123224.018.03.060.01.03130.0217.0233.0153.02227.07.029.01.07.07.060.008333
151133413.078.08.0391.064.02714.0231.0226.0128.01423.07.038.03.08.07.0396.203231
151143297.036.013.0420.0132.03474.0219.0211.0125.01983.07.039.03.08.07.0440.254472
151153328.0321.013.0323.012.05109.0186.0227.0180.03151.07.038.03.08.07.0323.222833
151163455.037.05.0841.092.0939.0220.0229.0146.0362.07.040.02.08.07.0846.017139
151173279.090.014.0404.0113.01513.0240.0218.0105.01503.07.029.01.07.07.0419.505661
151183589.0357.09.0418.052.01868.0205.0223.0155.01657.07.040.02.08.07.0421.222032
151193385.0345.015.0350.076.03625.0190.0216.0164.03327.07.040.03.08.07.0358.156390